ENSO and AMO manifolds

Basic geophysics to start.

AMO is measured in the north Atlantic, and influenced by an annual cycle — at a latitude that is inclined more to the Sun in the summer (peak declination is at summer solstice) than winter.

ENSO resides on the equator, subject to the topological constraints of that boundary condition. It therefore gets influenced by a northern hemisphere cycle and a southern hemisphere cycle. This turns into a semi-annual cycle.

Mechanical torques to the Earth’s rotation are measured by deviations in the Earth’s length-of-day (LOD) — see the time-series below1. There is a clear annual and semi-annual cycle apparent as evidenced in the top panel, and also a gradual multi-decadal variation. Much stronger underlying this variation is a steady lunar tidal cycling, see bottom panel, where it is most easily revealed by taking the time-derivative of LOD — a real torque, or instantaneous acceleration. This decomposes to fundamental Mf, Mm, and Mf ‘ tidal cycles, with the Mf and Mm interfering to create an 8.848y perigean cycle, and the Mf and Mf ‘ interfering to create an 18.6 year nodal cycle. These beat envelopes can clearly be seen in the lower panel, along with occasional disturbances related to El Nino events (e.g. very strong 1983, 1988, 1992, very strong 1998, 2008)

A graph showing the Length of Day (LOD) delta in milliseconds over the years from 1960 to 2010, with marked points at 1983.1, 1992.2, 1997.9, and 2007.3.

A geophysical ansatz cooperatively linking LOD changes to climate cycles such as El Nino (ENSO), lies in the annual and semi-annual impulses that likely reinforce the instantaneous tidal torque that occurs at that time2. The premise is that torque over an impulse duration leads to an incremental level shift in LOD and that generates an internal (i.e. hidden) latent forcing manifold for the ocean’s fluid dynamics. This is particularly sensitive along the subsurface thermocline, where effective gravity is reduced.

Consider the northern Atlantic first. The forcing manifold is generated via a convolution (i.e. essentially integrated) of the annual impulses with the tidal torque at that instance. The strongest constituent tidal factor Mf shown below generates a ~3.8 year cycle over time as it alternates between reinforcing or canceling in sign. The value of 3.8 is determined via modulo arithmetic, 365.242/13.66 mod 1 ~ 3.8. Similarly, the Mf ‘ and Mm lead to ~4.8 and 3.9 year cycles.

A bar chart displaying various amplitude values across different categories labeled Mf', Mm, Mt, and others, with the highest amplitude represented by Mf and a smaller peak for Mt'.

These are the strongest cycles by amplitude, but due to a fortuitous commensurate alignment with the annual signal, the Mt tidal has a significant impact on the shape of the manifold. The fact that 40 of the 9.133 Mt tidal cycles fit almost precisely into a year means that constructive interferences gradually accumulate over a 60-year period and then change sign and decrement over the next 60-year interval. This rides on top of the faster 3.8, 3.9, and 4.8 year cycles creating an erratic staircase as shown below. There is a behavior known in fluid dynamics called a devil’s staircase which likely has a meaningful relationship to this form.3

Line graph depicting the values of a latent or hidden forcing layer over the years from 1880 to 2022, with a light blue background and red lines illustrating fluctuations in the data.

But this is just the manifold, a forcing that can be considered as almost a phase envelope — we are not yet seeing the oceanic basin’s response to his forcing. That’s why considering it a phase makes intuitive sense, as the response may simply be a sine wave acting on this phase, i.e. A sin (k*phase)+ B cos(k*phase) where k is a constant. This is where the fluid dynamics mathematics of Laplace’s Tidal Equations (LTE) and LTE modulation fits in, as described in detail in Chapter 12 of Mathematical Geoenergy2. That text provides a non-intuitive grounding to what until now has a first-order physics explanation.

To get a feel for what this — in reality a non-linear response — involves computationally, consider the modulation/transfer function shown below:

A diagram illustrating the relationship between manifold input (blue curve), modulation/transfer function (red curve), and system response (green curve) over time. It includes panels with labeled points indicating key features of the functions.

That’s what it looks like with a k-modulation over the entire phase envelope, as it essentially doubles the frequency, changing the 120-year cycle to a 60-year cycle — not coincidentally the same period as the multidecadal period of the AMO.

Yet that 60-year cycle is only a single feature of the AMO, which is also characterized by wildly erratic fluctuations in value that almost obscure the multidecadal envelope. What actually works better as a model is if the shorter Mf steps on the staircase resolve to a complete single-period sinusoidal response, what in mathematical parlance is referred to a winding number of 1. The effective model thus becomes sin(kϕ+ϵsin(kϕ+ϕ1)+ϕ2)\sin(k \phi + \epsilon \sin(k \phi+\phi_1) +\phi_2), where ϕ\phi = phase, shown below for k = 1.55 and ϵ\epsilon = 0.5. The phase slippage due to Mt causes the response to wander about zero over several decades.

Graph showing AMO time series with model and data comparisons, including regression analysis, latent forcing layer, running windowed correlation, and power spectrum.

The mapping to LOD remains largely intact through this as the 18.6 year and 8.85 year envelope is still clear. Note that a model of LOD is required to extrapolate before 1962, when the first LOD precision measurements were available.

A time series plot showing the standardized values of two distinct factors from 1960 to 2020, illustrated with varying shades of blue.

The monthly and fortnightly remained near the same, but longer term tidal factors greater than 1 year in period had to be included (see bars in gray below), ostensibly to accommodate the drift in LOD estimated over the ~150 year time span of AMO.

Bar chart showing absolute amplitudes of 32 tidal factors related to AMO, with varying heights representing different amplitude values.

The promise of the LOD-calibrated mathematical modeling further explains how the AMO itself may feed back into the LOD itself, as many have noted the multi-decadal variation in LOD resembles that of AMO5


The next step is to evaluate NINO34 (i.e. ENSO), lying along the equator. The distinction here is that the annual impulse, used for AMO, must be converted to a semi-annual impulse (one positive [+] excursion alternating with one negative [-] excursion). Note that the semi-annual nature destroys the constructive interference of the Mt ordinal, which created the 120-year staircase. Instead, we have a strong ~3.8yr up and down devil’s staircase manifold. So, we can evaluate the following chart — middle left shows the estimated manifold and middle right shows the sin(k) LTE modulation applied to achieve the top panel left model fit in red.

Time series analysis for Site nino4, displaying model and data trends from 1880 to 2022.

The semi-annual forcing is doing as predicted — it breaks the Mt-driven secular 120-year build-up and replaces it with a bounded, alternating step manifold that behaves like an ENSO-scale oscillator rather than an AMO staircase.

  • Top left: the red model tracks the broad phase/envelope of the blue NINO4 series well.
  • Top right: the scatter is clearly elongated along a positive slope, so the fit is not accidental. Some of the validation points are outside the regression set.
  • Middle left: this is the key result. The latent forcing is no longer a undulating staircase; it sits on recurring discrete bands, mostly between about -1.6 and -0.3, with occasional jumps toward 0 to +0.3. That is exactly the signature of the alternating +/- semi-annual impulse: an up/down “devil’s staircase” rather than constructive accumulation.
  • Middle right: the red sin(k) modulation overlays the dominant blue bands fairly well, especially on the main latent levels, so the modulation is using the tightened manifold. It occasionally underrepresents rare extremes.
  • Bottom left: the 50-month running correlation is usually high (~0.6–0.9) but drops down in the cross-validation interval. This could mean that the response is intermittently organized rather than uniformly phase-locked.
  • Bottom right: the PSD match is strongest at the low-order peaks; model and data line up well at the main maxima, while the data keeps more high-frequency power than the model. So the semi-annual latent structure captures the core resonant bands, but not all of ENSO’s fast variance.

Bottom line: this figure supports the idea that for equatorial ENSO/NINO4, the correct latent driver is a semi-annual alternating pulse, yielding a compact, oscillatory ~3.8-year staircase manifold instead of the long constructive Mt staircase used for AMO/NAO. The manifold looks physically coherent and the modulation is plausible, but the weak validation window says the current mapping is still less robust and more regime-dependent than the AMO case.

An amazing concordance is that the k = 1.55 and ϵ\epsilon = 0.5 are essentially the same for ENSO as for AMO, indicating this is likely a common-mode temporal response. It’s possible that these are related to Arnold tongue resonances6 In terms of plausibility and parsimony of these preliminary results, note how modest the Arnold winding is on the middle right panel (winding=2 suggests one winding for northern hemisphere and one for southern hemisphere) which may be related to the topological time reversal symmetry rules of the equatorial region7. If the equatorial latent manifold were showing many wraps, that would look more like a flexible fitting device; a winding of about 2 is close to the minimal nontrivial topology you would expect for an equatorial interface problem.

The reason this is plausible is that the equator is special: the Coriolis sign flips across it, so north and south contributions should enter with opposite handedness rather than accumulate into the same long constructive winding. In that setting, a two-sheet / two-turn organization — one branch associated with the northern side, one with the southern side — is a natural first-order picture. That the interpretation of the middle-right panel: the modulation is not over-twisted; it is just wrapped enough to separate the main latent bands and recover the top-left fit. That is also consistent with the Delplace/Marston style topological view of the equator as an interface where symmetry strongly constrains admissible structure.

On parsimony, this is good news. The model already uses a semi-annual sign-alternating impulse, which by itself
suppresses the Mt constructive staircase and forces a bounded oscillatory manifold. Once that choice is made, a
small winding number is the simplest way to map that latent staircase into ENSO-like oscillation. So, the topology is
doing real work without needing a large number of wraps, a high-order phase map, or a visually baroque modulation.


So, what would one expect for PDO? Since it inhabits the northern Pacific, one would expect an annual impulse. Borrowing the parameters from AMO, it fits the pattern cleanly with a sharply delineated LTE modulation. Note that even though PDO is considered to have some of the character of ENSO, the fact that the k = 1.55 and ϵ\epsilon = 0.5 parameters are again the same, indicates the common-mode behavior of these climate indices.

Time series plot of PDO data from 1880 to 2020, showing model and actual data with varying values.

NAO — North Atlantic Oscillation

Time series graph displaying NAO data from 1880 to 2026, showing model and actual data in blue and red.

It does also work for coastal mean sea level (MSL) tidal stations : Ratan, Sweden

Time series graph for Site #88 showing data and model values over the years 1900 to 2026.

IOD East (Indian Ocean Dipole) — Letting it free fit drove the Mt amplitude to a lower value. This indicated that the 120-year cycle was weaker, so adjusted this by adding a partial semi-annual component of -2/3 the amplitude of the annual impulse. The Indian Ocean straddles the equator but Asia to the north really clips off that lobe.

Time series graph for Site#iode with validation results representing model and data from 1880 to 2020.

TNA (Tropical North Atlantic) — has characteristics of AMO

A series of four graphs displaying time series data, model performance, and correlation analysis over a timeline from 1880 to 2026, featuring various statistical metrics and modulations.

TSA (Tropical South Atlantic). Is this more like ENSO?

Time series data chart showing cross-validation results from 1880 to 2026, including model and data comparisons.
World map highlighting the West Coast of the USA with 24 active stations shown in red out of a total of 51 stations.

Set of west coast MSL sites

Line graph displaying time series data from Site#wcoast, showing model and data values over the years from 1880 to 2022.

All the tidal factors were allowed to vary as that was the easiest way to optimize and escape local minimum, but the distribution of weightings remained roughly the same in the 9 cases fitted above. Since the k and ϵ\epsilon values also stayed even tighter, it’s possible that the cyclic fingerprint of each index is a combination of slightly different tidal factor contributions and the balance between annual and semi-annual impulses for that geospatial location. In fact, it might turn out that a more efficient fitting process is to start from a Bayesian-average tidal factor configuration instead of from the LOD calibration. This would reinforce the idea that this is truly a commo-mode behavior.

A grid of nine bar charts, each displaying data trends with horizontal bars in blue and grey.
Eye-chart of tidal factor weightings
top row: NINO4, AMO, PDO
middle: NAO, IOD, TNA
bottom : Ratan, TSA, West Coasr
Tidal Amplitude Spectrum (Complete)

Tidal Amplitude Spectrum

Complete dataset with all 32 periods from each directory
Dataset Summary: All 9 directories (nino4, amo, pdo, nao, iode, tsa, tna, 88, wcoast) with identical period structure. Periods > 365 days shown in gray background.
Loading complete dataset…

Note: Gray background indicates periods > 365 days (long periods). White background indicates periods ≤ 365 days (short periods).

Important: Values are amplitudes from the lt.exe.p JSON files. Negative values indicate phase differences.

Total entries: 32 periods × 9 directories = 288 amplitude values displayed.

Graph depicting composite sinusoidal waves from 1860 to 1970, showing standardized normalized amplitudes over time. The plot features labeled axes with date markers and multiple colored curves representing different waveforms.
The IOD-East and TSA, both south of the equator are close to the LOD composite


Is this the deeper physics?

Delplace, Marston, and Venaille showed that equatorial Kelvin and Yanai waves arise as topologically protected edge modes, associated with a bulk Chern number of 2 for the rotating shallow‑water Poincaré spectrum in (k,ω) space. Their result is an abstract existence theorem: it guarantees robust equatorial waves but does not specify how they are forced or parameterized in time for prediction. In contrast, the LTE manifold used here selects an equatorial standing mode consistent with that topology and embeds it in a time‑domain, lunisolar‑forced framework, with explicit annual impulses and nonlinear modulation fitted directly to ENSO, AMO, and tide‑gauge records. In this sense, the LTE formulation provides a practical parameterization that connects the topological structure of equatorial waves to applied, data‑driven prediction in physical time.

From a dynamical‑systems perspective, the LTE manifold treats ENSO and related indices as the response of a phase‑locked forced oscillator, living in a low‑dimensional latent space and driven by a small set of quasi‑periodic forcings (lunisolar tides plus an annual impulse). In the language of nonlinear dynamics, this is an explicitly parameterized instance of mode locking on a torus (Arnold tongues, Devil’s staircase, Farey‑ordered p:q plateaus), while in the language of topological fluids it corresponds to driving a protected equatorial edge mode (Kelvin/Yanai‑like) selected by the bulk Chern structure of the rotating shallow‑water system. In modern ML terms, the construction is a physics‑informed analogue of SINDy/KAN latent‑manifold models 8: a shared, low‑dimensional latent driver is specified a priori, and simple nonlinear mappings (amplitude, phase, sinusoidal folding) are fitted to map that latent trajectory into many observed time series, providing an interpretable bridge between abstract topological theory and data‑driven prediction.

There is enough here for ML to extend, but the proviso is that the detailed LOD forcing must be applied -- I don't think it will work unless enough of the constituent tidal factors (ranked strongest to weakest) are included. The complexity of ENSO or AMO is a result of a Mach-Zehnder-like encryption of an already multi-constituent cycle - that's essentially impossible to decode without a valid manifold key.


References

  1. hpiers.obspm.fr/eop-pc/products/combined/C04.php?date=3&eop=3&year1=1962&month1=1&day1=1&year2=2012&month2=12&day2=31&SUBMIT=Submit+Search ↩︎
  2. Mathematical Geoenergy, Pukite, P.R. et al, (Wiley/AGU, 2019) ↩︎
  3. Arnold tongue - Wikipedia ↩︎
  4. Mathematical Geoenergy, Pukite, P.R. et al, (Wiley/AGU, 2019) ↩︎
  5. Marcus, S. L., 2016: Does an Intrinsic Source Generate a Shared Low-Frequency Signature in Earth’s Climate and Rotation Rate?. Earth Interact.20, 1–14, https://doi.org/10.1175/EI-D-15-0014.1.. ↩︎
  6. see 2. ↩︎
  7. Delplace, Marston, Topological origin of equatorial waves. Science 358,1075-1077(2017). DOI:10.1126/science.aan8819 ↩︎
  8. SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks
    AA HowardN ZolmanB JacobSL BruntonP Stinis
    arXiv preprint arXiv:2603.18548, 2026•arxiv.org ↩︎

Deep Seek of Wind + ENSO

This is a thread with DeepSeek, my prompts in italics bold. It was highly critical in ways that appeared very articulate, but it started to acquiesce when supplied with additional information. Not sure if this is due to a sliding or widening context window. In the past, one can try to argue about established physics with an LLM and it typically won’t budge.

The previous post was a discussion with the Gemini LLM and that seemed to be more agreeing and eventually convinced that a topological insulator approach seemed the best fit, which Deep Seek acknowledged as promising but did not use as a complete framing. Grade: Deep Seek – A for toughness, Gemini – B+ for diving into the data

Het Mehta on X: “most people think bigger context window = smarter model it doesn’t it just means more tokens to confuse it what’s the dumbest thing you’ve seen an llm forget mid-conversation?” / X

Wind drag and ENSO ocean dynamics

Is this realistic to think that the wind “drags the hot surface water from East Pacific and piles it up in the West Pacific” is that which causes the ENSO phenomenon? How much volume would this displace and is it even plausible? We know that can happen with a lake, yet that is a more confined basin.

This is an excellent and insightful question. You’ve zeroed in on a common oversimplification of ENSO (El Niño-Southern Oscillation) and are asking whether the “dragging and piling” mechanism is literally plausible across an entire ocean basin.

Let’s break it down.

The Short Answer

The idea that trade winds directly drag a significant volume of warm surface water from the East Pacific to the West Pacific, piling it up like water against one side of a bathtub, is an intuitive but physically incomplete picture of what causes ENSO. However, it captures a real, measurable effect — but the volume displaced is surprisingly small relative to the basin, and the “piling” is tiny compared to other forces. The real driver of ENSO is not just wind dragging water, but wind changing the slope of the thermocline (the boundary between warm surface water and cold deep water).

Let’s do the math and physics.

Continue reading

Current status of research according to AI

explain the Pukite tidal theory

The Pukite Tidal Theory, primarily developed by researcher Paul Pukite, proposes that long-period tidal forcing is the underlying driver for several major geophysical and atmospheric cycles that have previously been considered erratic or unresolved. [1, 2]

The core of the theory is that small gravitational perturbations from the Moon and Sun, which are perfectly predictable, are “aliased” or modulated by seasonal cycles to create the complex behaviors seen in Earth’s systems. [3, 4]

Key Phenomena Addressed

Pukite applies this model to three main “unresolved mysteries” in geophysics:

  • Quasi-Biennial Oscillation (QBO): A regular reversal of stratospheric winds. The theory argues that lunar nodal cycles, when combined with the annual solar cycle, create the roughly 28-month QBO period through a process called physical aliasing.
  • El Niño Southern Oscillation (ENSO): An erratic oceanic temperature cycle. The model suggests ENSO is a “sloshing” response of the ocean to tractive gravitational forces, essentially treating it as a solution to Laplace’s Tidal Equations.
  • Chandler Wobble: A small deviation in the Earth’s axis of rotation. The theory posits this is caused by an external lunar torque rather than internal Earth dynamics. [1, 2, 3, 5, 6, 7]

How the Model Works

  1. Lunar Gravitational Potential: The model starts by calculating the precise lunar gravitational potential over time.
  2. Seasonal Modulation: These fast lunar cycles are provoked by a seasonal (yearly) peak in energy. This “carrier” signal helps filter out short-term fluctuations and reveals long-term patterns.
  3. Physical Aliasing: Because these cycles are sampled or triggered by seasonal events (like solstices), the resulting data shows lower-frequency “aliased” harmonics that match the observed multi-year periods of ENSO and QBO. [4, 8, 9]

Scientific Reception

This theory is considered a novel and controversial alternative to standard geophysical models. While it offers high correlation with historical data, it has faced skepticism from mainstream physical oceanographers and meteorologists who argue that it may “shoehorn” complex fluid dynamics into a periodic framework. Much of the work is documented on the GeoEnergy Math blog and in discussion papers for the Earth System Dynamics (ESD) journal. [1, 4, 7, 10]

Would you like to see a more detailed breakdown of the mathematical aliasing used to explain the QBO’s 28-month cycle?

[1] https://esd.copernicus.org

[2] https://www.researchgate.net

[3] https://esd.copernicus.org

[4] https://geoenergymath.com

[5] https://acp.copernicus.org

[6] https://geoenergymath.com

[7] https://geoenergymath.com

[8] https://esd.copernicus.org

[9] https://geoenergymath.com

[10] https://esd.copernicus.org


This link may go stale, but here is the original response:

https://share.google/aimode/ta098ixUIyoNefp49

The perplexing Chandler wobble

I had heard that Perplexity was an LLM geared to scientific research, so I gave it this prompt:

Explain the Chandler wobble as due to a stroboscopic driving force emerging from the nonlinear modulation of an impulsed annual signal with the periodic declination torque of the lunar draconic cycle operating as a wavenumber=0 group symmetry.

The Chandler wobble can be viewed as a stroboscopically forced precession mode of Earth’s rotation, driven by a nonlinear interaction between an annually pulsed excitation and the lunar draconic (nodal) tide, with the effective forcing emerging in the band of the 433‑day free wobble. [1,2,3]

The behavioral characteristics include a cycle and damping:

  • The Chandler wobble is a free nutation of Earth’s rotation axis with a period of about 433 days and amplitude of order 0.05–0.2 arcsec, slightly different from the 305‑day Euler period because Earth is elastic and has oceans and a fluid core. [3,6,7]
  • In the absence of forcing, this mode would decay on a timescale of decades, so it must be continuously re‑excited by some geophysical process (mass redistribution in oceans/atmosphere, etc.). [3,4,5]

An annual impulsive forcing also generates an annual wobble. Think of the annual cycle as supplying near‑impulsive changes in the effective torque that can couple into polar motion, because seasonal mass shifts and circulation changes are strongest at certain times of year (e.g., around solstices). [1,3,5]

In a simplified model, this is approximated as an annual train of impulses in the excitation function of the polar motion equation rather than a smooth sinusoid. [1,2]

An impulse train has a flat, comb‑like spectrum: it projects power into many sidebands around any underlying carrier frequency it modulates. When that carrier is the lunar draconic tide, the sidebands can “step” into the Chandler band by aliasing and nonlinear mixing. [1,2]

The draconic (nodal) tide acts as a modulator to force sub-band cycles on the annual carrier. The lunar draconic cycle (node–to–node motion of the Moon’s orbit relative to the equatorial plane) produces a strong, periodic variation in declination and thus in the latitude pattern of tidal torque on Earth.  The relevant subharmonic here is the draconic fortnight (≈13.606 days), which governs how the lunar tidal bulge migrates north–south. [1]

If you take that relatively high frequency draconic signal and modulate it with a biannual or annual envelope tied to the solar geometry (solstice alignment of Sun–Moon–Earth), you get an amplitude‑modulated carrier. In Fourier terms, that modulation splits the draconic line into a comb of spectral lines spaced by ± n × (1 yr) around the original frequency. [1,2]

Because Earth’s free Chandler frequency sits far below the raw draconic frequency, you don’t resonate with the carrier itself; instead you hit the Chandler band through an aliasing condition: one of those sidebands (carrier ± k×annual) falls extremely close to the Chandler frequency (≈0.843 cycles/yr), so energy is pumped efficiently into the wobble. [1,2]

There’s a stroboscopic viewpoint to aid in understanding. From a stroboscope’s perspective, the annual (or twice‑annual) sampling by the Sun–Earth geometry “views” the draconic torque at almost, but not exactly, the same phase each year.  This is analogous to watching a fast‑spinning wheel under a flashing light: if the flash frequency is close but not equal to the spin frequency, you see a slow apparent beat motion. The slow beat corresponds here to the Chandler period, and the flashing is the annually pulsed excitation. [1,2]

Mathematically, the product of a fast oscillation (draconic tide) and an impulsive annual envelope produces cross‑terms whose effective frequency is the difference between the draconic and integer multiples of the annual frequency. One of these difference frequencies lands in the Chandler band, so in the rotating‑frame equations for polar motion it appears as a nearly resonant, narrowband forcing. [1,2,3]

Concerning the Wavenumber = 0 group symmetry: Polar motion is effectively a global (degree‑1, order‑1 in spherical harmonics) displacement of Earth’s rotation axis with respect to its figure; the Chandler mode is a normal mode of the whole body. [5,6,7]

The particular draconic–annual interaction you describe can be interpreted as exciting the azimuthally symmetric part of the torque pattern—no preferred longitude, just a latitude‑dependent load—which in spectral language is a wavenumber‑0 (axisymmetric) component in Earth’s rotating frame. [1,6]

Calling it a “wavenumber=0 group symmetry” is a way of saying: after averaging over the fast draconic and diurnal phases, what survives is the group‑velocity‑scale envelope of the forcing, which is spatially uniform in longitude and thus couples efficiently to the global Chandler eigenmode while filtering out higher‑wavenumber patterns that would average to zero. [1,6,7]

So in compact form: the Chandler wobble emerges as a resonant response of Earth’s free polar mode to a stroboscopically generated, axisymmetric forcing produced by nonlinear mixing of an annually impulsed excitation with the lunar draconic tide, whose aliased sideband falls at the Chandler frequency. [1,2,3]

(continuation prompt)

What mathematical formula derives the 433-day period from draconic aliasing?

You can get the 433‑day Chandler period as a beat (alias) period between the draconic fortnightly tide and the annual cycle by using the standard aliasing / beat‑frequency relation in frequency space. [1,2,3]

1. Set up the frequencies.  Let

Draconic fortnight period (half draconic month)
T_{d} = 27.2122/2 \approx 13.6061 days. [1,2]

Annual period
T_{y} = 365.242] days. [1,4]

Convert to frequencies (in cycles per day):

f_d = \frac{1}{T_d},   f_y = \frac{1}{T_y}

Numerically, [1,2]

f_d  = \frac{1}{13.60611} =  0.0735 c/d,   f_y = \frac{1}{365.2421} = 0.0027379 c/d.

2. Alias (beat) with annual sampling is when you modulate or impulsively sample the draconic signal annually, sidebands appear at

f = |f_d - n f_y|,  n = 1, 2, 3,  . . .

The effective long period seen in the slowly varying envelope (the alias period) is

T_\text{alias} = \frac{1}{|f_d - n f_y|}

Choose integer [n] such that T_{\text{alias}} lands near 433 days. In the GeoEnergy Math formulation this corresponds to the harmonic that comes out closest to the Chandler band, giving

T_\text{alias} = 432.77 \text{days} = 1.185 \text{years},

which matches the observed Chandler wobble period. [1,2,3]

So, in words: the mathematical formula is the beat‑period relation

T_{\text{alias}} = 1 / |f_{d} - n f_{y}|,

with f_{d} from the 13.606‑day draconic fortnight and f_{y} from the annual cycle, and for the appropriate harmonic (n) this evaluates to ≈ 433 days. [1,2,3]

Refs

  1. https://geoenergymath.com/2017/03/04/the-chandler-wobble-challenge/
  2. https://geoenergymath.com/2017/05/21/the-lunar-geophysical-connection/
  3. https://esd.copernicus.org/preprints/esd-2020-74/esd-2020-74-AC5.pdf
  4. https://en.wikipedia.org/wiki/Chandler_wobble
  5. http://landmarks.allenbrowne.info/LatitudeObservatory/ChandlerWobble.htm
  6. https://legacy.iho.int/iho_pubs/CB/C-33/C-33_maree_simon_en.pdf
  7. https://www.technologyreview.com/2009/08/31/265599/earths-chandler-wobble-changed-dramatically-in-2005/


So, it basically summarized easily available sources from the internet, mainly from what I had written before.

Hidden latent manifolds in fluid dynamics

The behavior of complex systems, particularly in fluid dynamics, is traditionally described by high-dimensional systems of equations like the Navier-Stokes equations. While providing practical applications as is, these models can obscure the underlying, simplified mechanisms at play. It is notable that ocean modeling already incorporates dimensionality reduction built in, such as through Laplace’s Tidal Equations (LTE), which is a reduced-order formulation of the Navier-Stokes equations. Furthermore, the topological containment of phenomena like ENSO and QBO within the equatorial toroid , and the ability to further reduce LTE in this confined topology as described in the context of our text Mathematical Geoenergy underscore the inherent low-dimensional nature of dominant geophysical processes. The concept of hidden latent manifolds posits that the true, observed dynamics of a system do not occupy the entire high-dimensional phase space, but rather evolve on a much lower-dimensional geometric structure—a manifold layer—where the system’s effective degrees of freedom reside. This may also help explain the seeming paradox of the inverse energy cascade, whereby order in fluid structures seems to maintain as the waves become progressively larger, as nonlinear interactions accumulate energy transferring from smaller scales.

Discovering these latent structures from noisy, observational data is the central challenge in state-of-the-art fluid dynamics. Enter the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, pioneered by Brunton et al. . SINDy is an equation-discovery framework designed to identify a sparse set of nonlinear terms that describe the evolution of the system on this low-dimensional manifold. Instead of testing all possible combinations of basis functions, SINDy uses a penalized regression technique (like LASSO) to enforce sparsity, effectively winnowing down the possibilities to find the most parsimonious, yet physically meaningful, governing differential equations. The result is a simple, interpretable model that captures the essential physics—the fingerprint of the latent manifold. The SINDy concept is not that difficult an algorithm to apply as a decent Python library is available for use, and I have evaluated it as described here.

Applying this methodology to Earth system dynamics, particularly the seemingly noisy, erratic, and perhaps chaotic time series of sea-level variation and climate index variability, reveals profound simplicity beneath the complexity. The high-dimensional output of climate models or raw observations can be projected onto a model framework driven by remarkably few physical processes. Specifically, as shown in analysis targeting the structure of these time series, the dynamics can be cross-validated by the interaction of two fundamental drivers: a forced gravitational tide and an annual impulse.

The presence of the forced gravitational tide accounts for the regular, high-frequency, and predictable components of the dynamics. The annual impulse, meanwhile, serves as the seasonal forcing function, representing the integrated effect of large-scale thermal and atmospheric cycles that reset annually. The success of this sparse, two-component model—where the interaction of these two elements is sufficient to capture the observed dynamics—serves as the ultimate validation of the latent manifold concept. The gravitational tides with the integrated annual impulse are the discovered, low-dimensional degrees of freedom, and the ability of their coupled solution to successfully cross-validate to the observed, high-fidelity dynamics confirms that the complex, high-dimensional reality of sea-level and climate variability emerges from this simple, sparse, and interpretable set of latent governing principles. This provides a powerful, physics-constrained approach to prediction and understanding, moving beyond descriptive models toward true dynamical discovery.

An entire set of cross-validated models is available for evluation here: https://pukpr.github.io/examples/mlr/.

This is a mix of climate indices (the 1st 20) and numbered coastal sea-level stations obtained from https://psmsl.org/

https://pukpr.github.io/examples/map_index.html

  • nino34 — NINO34 (PACIFIC)
  • nino4 — NINO4 (PACIFIC)
  • amo — AMO (ATLANTIC)
  • ao — AO (ARCTIC)
  • denison — Ft Denison (PACIFIC)
  • iod — IOD (INDIAN)
  • iodw — IOD West (INDIAN)
  • iode — IOD East (INDIAN)
  • nao — NAO (ATLANTIC)
  • tna — TNA Tropical N. Atlantic (ATLANTIC)
  • tsa — TSA Tropical S. Atlantic (ATLANTIC)
  • qbo30 — QBO 30 Equatorial (WORLD)
  • darwin — Darwin SOI (PACIFIC)
  • emi — EMI ENSO Modoki Index (PACIFIC)
  • ic3tsfc — ic3tsfc (Reconstruction) (PACIFIC)
  • m6 — M6, Atlantic Nino (ATLANTIC)
  • m4 — M4, N. Pacific Gyre Oscillation (PACIFIC)
  • pdo — PDO (PACIFIC)
  • nino3 — NINO3 (PACIFIC)
  • nino12 — NINO12 (PACIFIC)
  • 1 — BREST (FRANCE)
  • 10 — SAN FRANCISCO (UNITED STATES)
  • 11 — WARNEMUNDE 2 (GERMANY)
  • 14 — HELSINKI (FINLAND)
  • 41 — POTI (GEORGIA)
  • 65 — SYDNEY, FORT DENISON (AUSTRALIA)
  • 76 — AARHUS (DENMARK)
  • 78 — STOCKHOLM (SWEDEN)
  • 111 — FREMANTLE (AUSTRALIA)
  • 127 — SEATTLE (UNITED STATES)
  • 155 — HONOLULU (UNITED STATES)
  • 161 — GALVESTON II, PIER 21, TX (UNITED STATES)
  • 163 — BALBOA (PANAMA)
  • 183 — PORTLAND (MAINE) (UNITED STATES)
  • 196 — SYDNEY, FORT DENISON 2 (AUSTRALIA)
  • 202 — NEWLYN (UNITED KINGDOM)
  • 225 — KETCHIKAN (UNITED STATES)
  • 229 — KEMI (FINLAND)
  • 234 — CHARLESTON I (UNITED STATES)
  • 245 — LOS ANGELES (UNITED STATES)
  • 246 — PENSACOLA (UNITED STATES)

Crucially, this analysis does not use the SINDy algorithm, but a much more basic multiple linear regression (MLR) algorithm predecessor, which I anticipate being adapted to SINDy as the model is further refined. Part of the rationale for doing this is to maintain a deep understanding of the mathematics, as well as providing cross-checking and thus avoiding the perils of over-fitting, which is the bane of neural network models.

Also read this intro level on tidal modeling, which may form the fundamental foundation for the latent manifold: https://pukpr.github.io/examples/warne_intro.html. The coastal station at Wardemunde in Germany along the Baltic sea provided a long unbroken interval of sea-level readings which was used to calibrate the hidden latent manifold that in turn served as a starting point for all the other models. Not every model works as well as the majority — see Pensacola for a sea-level site and and IOD or TNA for climate indices, but these are equally valuable for understanding limitations (and providing a sanity check against an accidental degeneracy in the model fitting process) . The use of SINDy in the future will provide additional functionality such as regularization that will find an optimal common-mode latent layer,.

Simpler models can outperform deep learning at climate prediction

This article in MIT News:

https://news.mit.edu/2025/simpler-models-can-outperform-deep-learning-climate-prediction-0826

“New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.” … “While deep learning has become increasingly popular for emulation, few studies have explored whether these models perform better than tried-and-true approaches. The MIT researchers performed such a study. They compared a traditional technique called linear pattern scaling (LPS) with a deep-learning model using a common benchmark dataset for evaluating climate emulators. Their results showed that LPS outperformed deep-learning models on predicting nearly all parameters they tested, including temperature and precipitation.

Machine learning and other AI approaches such as symbolic regression will figure out that natural climate variability can be done using multiple linear regression (MLR) with cross-validation (CV), which is an outgrowth or extension of linear pattern scaling (LPS).

https://pukpr.github.io/results/image_results.html

When this was initially created on 9/1/2025, there were 3000 CV results on time-series
that averaged around 100 years (~1200 monthly readings/set) so over 3 million data points

In this NINO34 (ENSO) model, the test CV interval is shown as a dashed region

I developed this github model repository to make it easy to compare many different data sets, much better than using an image repository such as ImageShack.

There are about 130 sea-level height monitoring stations in the sites, which is relevant considering how much natural climate variation a la ENSO has an impact on monthly mean SLH measurements. See this paper Observing ENSO-modulated tides from space

“In this paper, we successfully quantify the influences of ENSO on tides from multi-satellite altimeters through a revised harmonic analysis (RHA) model which directly builds ENSO forcing into the basic functions of CHA. To eliminate mathematical artifacts caused by over-fitting, Lasso regularization is applied in the RHA model to replace widely-used ordinary least squares. “

Model Ontology

In Chapter 10 of the book we touch on organization of environmental models.

“Furthermore, by applying ontology‐based approaches for organizing models and techniques, we can set the stage for broader collections of such models discoverable by a general community of designers and analysts. Together with standard access protocols for context modeling,
these innovations provide the promise of making environmental context models generally available and reusable, significantly assisting the energy analyst.”

Energy Transition : Applying Probabilities and Physics

Although we didn’t elaborate on this topic, it is an open area for future development, as our 2017 AGU presentation advocates. The complete research report is available as https://doi.org/10.13140/RG.2.1.4956.3604.

What we missed on the first pass was an ontology for citations titled CiTO (Citation Typing Ontology) which enables better classification and keeping track of research lineage. The idea again is to organize and maintain scientific knowledge for engineering and scientific modeling applications. As an example, one can readily see how the Citation Typing Ontology could be applied, with the is_extended_by object property representing much of how science and technology advances — in other words, one finding leading to another.

The ENSO Forcing Potential – Cheaper, Faster, and Better

Following up on the last post on the ENSO forcing, this note elaborates on the math.  The tidal gravitational forcing function used follows an inverse power-law dependence, where a(t) is the anomalistic lunar distance and d(t) is the draconic or nodal perturbation to the distance.

F(t) propto frac{1}{(R_0 + a(t) + d(t))^2}'

Note the prime indicating that the forcing applied is the derivative of the conventional inverse squared Newtonian attraction. This generates an inverse cubic formulation corresponding to the consensus analysis describing a differential tidal force:

F(t) propto -frac{a'(t)+d'(t)}{(R_0 + a(t) + d(t))^3}

For a combination of monthly and fortnightly sinusoidal terms for a(t) and d(t) (suitably modified for nonlinear nodal and perigean corrections due to the synodic/tropical cycle)   the search routine rapidly converges to an optimal ENSO fit.  It does this more quickly than the harmonic analysis, which requires at least double the unknowns for the additional higher-order factors needed to capture the tidally forced response waveform. One of the keys is to collect the chain rule terms a'(t) and d'(t) in the numerator; without these, the necessary mixed terms which multiply the anomalistic and draconic signals do not emerge strongly.

As before, a strictly biennial modulation needs to be applied to this forcing to capture the measured ENSO dynamics — this is a period-doubling pattern observed in hydrodynamic systems with a strong fundamental (in this case annual) and is climatologically explained by a persistent year-to-year regenerative feedback in the SLP and SST anomalies.

Here is the model fit for training from 1880-1980, with the extrapolated test region post-1980 showing a good correlation.

The geophysics is now canonically formulated, providing (1) a simpler and more concise expression, leading to (2) a more efficient computational solution, (3) less possibility of over-fitting, and (4) ultimately generating a much better correlation. Alternatively, stated in modeling terms, the resultant information metric is improved by reducing the complexity and improving the correlation — the vaunted  cheaper, faster, and better solution. Or, in other words: get the physics right, and all else follows.

 

 

 

 

 

 

 

 

 

 

 

 

 

Solar Eclipse 2017 : What else?

The reason we can so accurately predict the solar eclipse of 2017 is because we have accurate knowledge of the moon’s orbit around the earth and the earth’s orbit around the sun.

Likewise, the reason that we could potentially understand the behavior of the El Nino Southern Oscillation (ENSO) is that we have knowledge of these same orbits. As we have shown and will report at this year’s American Geophysical Union (AGU) meeting, the cyclic gravitational pull of the moon (lower panel in Figure 1 below) interacting seasonally precisely controls the ENSO cycles (upper panel Figure 1).

Fig 1: Training interval 1880-1950 leads to extrapolated fit post-1950

Figure 2 is how sensitive the fit is to the precise value of the lunar cycle periods. Compare the best ft values to the known lunar values here. This is an example of the science of metrology.

Fig 2: Sensitivity to selection of lunar periods.

The implications of this research are far-ranging. Like knowing when a solar eclipse occurs helps engineers and scientists prepare power utilities and controlled climate experiments for the event, the same considerations apply to ENSO.  Every future El Nino-induced heat-wave or monsoon could conceivably be predicted in advance, giving nations and organizations time to prepare for accompanying droughts, flooding, and temperature extremes.

Follow @whut on Twitter:

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ENSO Split Training for Cross-Validation

If we split the modern ENSO data into two training intervals — one from 1880 to 1950 and one from 1950 to 2016, we get roughly equal-length time series for model evaluation.

As Figure 1 shows, a forcing stimulus due to monthly-range LOD variations calibrated to the interval between 2000 to 2003 (lower panel) is used to train the ENSO model in the interval from 1880 to 1950. The extrapolated model fit in RED does a good job in capturing the ENSO data in the period beyond 1950.

Fig. 1: Training 1880 to 1950

Next, we reverse the training and verification fit, using the period from 1950 to 2016 as the training interval and then back extrapolating. Figure 2 shows this works about as well.

Fig. 2: Training interval 1950 to 2016

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